Variational Gaussian process for optimal sensor placement
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Sensor placement is an optimisation problem that has recently gained great relevance. In order to achieve accurate online updates of a predictive model, sensors are used to provide observations. When sensor location is optimally selected, the predictive model can greatly reduce its internal errors. A greedy-selection algorithm is used for locating these optimal spatial locations from a numerical embedded space. A novel architecture for solving this big data problem is proposed, relying on a variational Gaussian process. The generalisation of the model is further improved via the preconditioning of its inputs: Masked Autoregressive Flows are implemented to learn nonlinear, invertible transformations of the conditionally modelled spatial features. Finally, a global optimisation strategy extending the Mutual Information-based optimisation and fine-tuning of the selected optimal location is proposed. The methodology is parallelised to speed up the computational time, making these tools very fast despite the high complexity associated with both spatial modelling and placement tasks. The model is applied to a real three-dimensional test case considering a room within the Clarence Centre building located in Elephant and Castle, London, UK.
Date Issued
2021-04-01
Date Acceptance
2021-02-12
Citation
Applications of Mathematics, 2021, 66 (2), pp.287-317
ISSN
0373-6725
Publisher
Institute of Mathematics, Czech Academy of Sciences.
Start Page
287
End Page
317
Journal / Book Title
Applications of Mathematics
Volume
66
Issue
2
Copyright Statement
© 2021, Institute of Mathematics, Czech Academy of Sciences.
Sponsor
European Commission Directorate-General for Research and Innovation
Engineering & Physical Science Research Council (E
Grant Number
RG80519
Subjects
Science & Technology
Physical Sciences
Mathematics, Applied
Mathematics
sensor placement
variational Gaussian process
mutual information
0102 Applied Mathematics
Numerical & Computational Mathematics
Publication Status
Published
Date Publish Online
2021-02-12